Modified treatment of intercepted snow improves the simulated forest albedo in the Canadian Land Surface Scheme
Bibliographic record
Abstract
The Canadian Land Surface Scheme (CLASS) was modified to correct an underestimation of the winter albedo in evergreen needleleaf forests. Default values for the visible and near-infrared albedo of a canopy with intercepted snow, αVIS,cs and αNIR,cs, respectively, were too small, and the fraction of the canopy covered with snow, fsnow, increased too slowly with interception, producing a damped albedo response. A new model for fsnow is based on zI*, the effective depth of newly intercepted snow required to increase the canopy albedo to its maximum, which corresponds in the model with fsnow = 1. Snow unloading rates were extracted from visual assessments of photographs and modelled based on relationships with meteorological variables, replacing the time-based method employed in CLASS. These parameterizations were tested in CLASS version 3.6 at boreal black spruce and jack pine forests in Saskatchewan, Canada, a subalpine Norway spruce and silver fir forest at Alptal, Switzerland, and a boreal maritime forest at Hitsujigaoka, Japan. Model configurations were assessed based on the index of agreement, d, relating simulated and observed daily albedo. The new model employs αVIS,cs = 0.27, αNIR,cs = 0.38 and zI* = 3 cm. The best single-variable snow unloading algorithm, determined by the average cross-site d, was based on wind speed. Two model configurations employing ensemble averages of the unloading rate as a function of total incoming radiation and wind speed, and air temperature and wind speed, respectively, produced larger minimum cross-site d values but a smaller average. The default configuration of CLASS 3.6 produced a cross-site average d from October to April of 0.58. The best model employing a single parameter (wind speed at the canopy top) for modelling the unloading rate produced an average d of 0.86, while the two-parameter ensemble-average unloading models produced a minimum d of 0.81 and an average d of 0.84. © 2015 Her Majesty the Queen in Right of Canada. Hydrological Processes published by John Wiley & Sons, Ltd.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".